Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach

Maaref, Ahmad; Romero, Francisco; Montagnon, Emmanuel; Cerny, Milena; Nguyen, Bich; Vandenbroucke, Franck; Soucy, Geneviève; Turcotte, Simon; Tang, An; Kadoury, Samuel

doi:10.1007/s10278-020-00332-2

Cited by 17 publications

(27 citation statements)

References 25 publications

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“…A reliable assessment of response to chemotherapy is of paramount importance for the personalized treatment decision-making process to determine eligibility for surgery, or the need for second-line treatments[ 53 ]. Discriminating responsive from unresponsive nodules or new lesions on the CT scan often represents a challenging task for radiologists, therefore Maaref et al [ 54 ] developed a fully automated framework based on DL CNN that achieved an accuracy of 0.91 (95%CI: 0.88-0.93) for differentiating treated and untreated lesions, and 0.78 (95%CI: 0.74-0.83) for predicting the response to a FOLFOX + bevacizumab-based chemotherapy regimen. Similarly, the DL radiomics model by Wei et al [ 55 ] was able to predict response to chemotherapy (CAPEOX, mFOLFOX6, FOLFIRI or XELIRI regimens) of CRLM based on contrast-enhanced CT according to the response evaluation criteria in solid tumors with an AUC in the validation cohort of 0.820 (95%CI: 0.681-0.959) that increases to 0.830 (95%CI: 0.688-0.973) combining the DL-based model with the CEA serum level.…”

Section: Ai Models For Treated Crlmmentioning

confidence: 99%

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Rompianesi¹,

Pegoraro²,

Ceresa³

et al. 2022

WJG

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Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients’ survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients’ exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies’ role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.

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Section: Ai Models For Treated Crlmmentioning

confidence: 99%

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Rompianesi¹,

Pegoraro²,

Ceresa³

et al. 2022

WJG

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“…Their accuracies were significantly better than those of clinicians. Moreover, deep learning of radiological images exhibited its potential value in assessing chemosensitivity[ 64 , 65 , 151 ]. For example, a CNN system was trained using 202 cases with colorectal cancer liver metastases and was validated to have good accuracy for predicting responses to FOLFOX combined with bevacizumab regimens based on CT information[ 151 ].…”

Section: Achievements Of Ann Research In Gi Diseasesmentioning

confidence: 99%

“…Moreover, deep learning of radiological images exhibited its potential value in assessing chemosensitivity[ 64 , 65 , 151 ]. For example, a CNN system was trained using 202 cases with colorectal cancer liver metastases and was validated to have good accuracy for predicting responses to FOLFOX combined with bevacizumab regimens based on CT information[ 151 ]. The practicability of radiotherapy, anti-integrin therapy, traditional Chinese medicine, and immunotherapy could also be improved with the support of ANNs[ 152 - 155 ].…”

Section: Achievements Of Ann Research In Gi Diseasesmentioning

confidence: 99%

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Cao¹,

Zhang²,

Wei³

et al. 2021

WJG

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Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

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“…Peng et al [ 19 ] and Liu et al [ 20 ] similarly reached promising results using deep learning-based approaches for HCC. Temporal features [ 21 ] and deep learning [ 22 ] were also used to predict outcomes, but are sensitive to limited datasets without capturing links between lesion types. More importantly, these techniques only provide a binary classification and do not produce follow-up images which can be used to extract quantifiable parameters from perfusion analyses prior to TACE.…”

Section: Introductionmentioning

confidence: 99%

“…There is a clear need to produce robust forecasting methods allowing to re-treat patients with increased drug concentrations or changing paradigms. Previous methods are based on binary classification, with no capability to predict futures images from which physiological parameters of the tumor can be measured before treatment [ 22 ]. By capturing the relationships between tumoral modifications with TACE regimens in a domain translation framework with GCNs, this would allow to build additional knowledge on patient response to chemoembolization.…”

Section: Introductionmentioning

confidence: 99%

Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks

et al. 2021

Self Cite

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Magnetic resonance imaging (MRI) plays a critical role in the planning and monitoring of hepatocellular carcinomas (HCC) treated with locoregional therapies, in order to assess disease progression or recurrence. Dynamic contrast-enhanced (DCE)-MRI sequences offer temporal data on tumor enhancement characteristics which has strong prognostic value. Yet, predicting follow-up DCE-MR images from which tumor enhancement and viability can be measured, before treatment of HCC actually begins, remains an unsolved problem given the complexity of spatial and temporal information. We propose an approach to predict future DCE-MRI examinations following transarterial chemoembolization (TACE) by learning the spatio-temporal features related to HCC response from pre-TACE images. A novel Spatial-Temporal Discriminant Graph Neural Network (STDGNN) based on graph convolutional networks is presented. First, embeddings of viable, equivocal and non-viable HCCs are separated within a joint low-dimensional latent space, which is created using a discriminant neural network representing tumor-specific features. Spatial tumoral features from independent MRI volumes are then extracted with a structural branch, while dynamic features are extracted from the multi-phase sequence with a separate temporal branch. The model extracts spatio-temporal features by a joint minimization of the network branches. At testing, a pre-TACE diagnostic DCE-MRI is embedded on the discriminant spatio-temporal latent space, which is then translated to the follow-up domain space, thus allowing to predict the post-TACE DCE-MRI describing HCC treatment response. A dataset of 366 HCC’s from liver cancer patients was used to train and test the model using DCE-MRI examinations with associated pathological outcomes, with the spatio-temporal framework yielding 93.5% classification accuracy in response identification, and generating follow-up images yielding insignificant differences in perfusion parameters compared to ground-truth post-TACE examinations.

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Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach

Cited by 17 publications

References 25 publications

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks

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